scholarly journals AN AUTOMATED 3D INDOOR TOPOLOGICAL NAVIGATION NETWORK MODELLING

2015 ◽  
Vol II-2/W2 ◽  
pp. 47-53 ◽  
Author(s):  
A. Jamali ◽  
A. A. Rahman ◽  
P. Boguslawski ◽  
C. M. Gold
Keyword(s):  
Indoor Environment ◽  
Laser Scanning ◽  
3D Models ◽  
Indoor Navigation ◽  
Control Points ◽  
Network Modelling ◽  
Building Model ◽  
Building Models ◽  
Data Acquisition Process ◽  
Topological Navigation

Indoor navigation is important for various applications such as disaster management and safety analysis. In the last decade, indoor environment has been a focus of wide research; that includes developing techniques for acquiring indoor data (e.g. Terrestrial laser scanning), 3D indoor modelling and 3D indoor navigation models. In this paper, an automated 3D topological indoor network generated from inaccurate 3D building models is proposed. In a normal scenario, 3D indoor navigation network derivation needs accurate 3D models with no errors (e.g. gap, intersect) and two cells (e.g. rooms, corridors) should touch each other to build their connections. The presented 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. For reducing time and cost of indoor building data acquisition process, Trimble LaserAce 1000 as surveying instrument is used. The modelling results were validated against an accurate geometry of indoor building environment which was acquired using Trimble M3 total station.

scholarly journals EVALUATION OF MODEL RECOGNITION FOR GRAMMAR-BASED AUTOMATIC 3D BUILDING MODEL RECONSTRUCTION

2016 ◽  
Vol XLI-B4 ◽  
pp. 63-69
Author(s):  
Qian Yu ◽  
Petra Helmholz ◽  
David Belton
Keyword(s):  
Laser Scanning ◽  
Quality Evaluation ◽  
Evaluation Process ◽  
3D Models ◽  
Point Of View ◽  
Three Dimensions ◽  
Public And Private ◽  
Building Models ◽  
Evaluation Point ◽  
Increasing Demand

In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.

scholarly journals EVALUATION OF MODEL RECOGNITION FOR GRAMMAR-BASED AUTOMATIC 3D BUILDING MODEL RECONSTRUCTION

2016 ◽  
Vol XLI-B4 ◽  
pp. 63-69 ◽  
Author(s):  
Qian Yu ◽  
Petra Helmholz ◽  
David Belton
Keyword(s):  
Laser Scanning ◽  
Quality Evaluation ◽  
Evaluation Process ◽  
3D Models ◽  
Point Of View ◽  
Three Dimensions ◽  
Public And Private ◽  
Building Models ◽  
Evaluation Point ◽  
Increasing Demand

In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

2014 ◽  
Vol XL-4 ◽  
pp. 335-340 ◽  
Author(s):  
Q. Yu ◽  
P. Helmholz ◽  
D. Belton ◽  
G. West
Keyword(s):  
Laser Scanning ◽  
Data File ◽  
Formal Grammar ◽  
Model Reconstruction ◽  
Reconstruction Process ◽  
Building Model ◽  
Building Models ◽  
3D Model Reconstruction ◽  
Data Interchange ◽  
3D Shapes

The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account.

scholarly journals Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

2019 ◽  
Vol 11 (19) ◽  
pp. 2262 ◽  
Author(s):  
Cui ◽  
Li ◽  
Dong
Keyword(s):  
Indoor Environment ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Graph Cuts ◽  
3D Models ◽  
Base Station ◽  
Label Graph ◽  
Semantic Constraints ◽  
Signal Simulation

3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

scholarly journals Evaluations of Three-Dimensional Building Model Reconstruction from LiDAR Point Clouds and Single-View Perspective Imagery

2014 ◽  
Vol XL-5 ◽  
pp. 597-600 ◽  
Author(s):  
F. Tsai ◽  
H. Chang
Keyword(s):  
Laser Scanning ◽  
Semantic Analysis ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Conjugate Points ◽  
Model Reconstruction ◽  
Perspective View ◽  
Single View ◽  
Building Model

This paper briefly presents two approaches for effective three-dimensional (3D) building model reconstruction from terrestrial laser scanning (TLS) data and single perspective view imagery and assesses their applicability to the reconstruction of 3D models of landmark or historical buildings. The collected LiDAR point clouds are registered based on conjugate points identified using a seven-parameter transformation system. Three dimensional models are generated using plan and surface fitting algorithms. The proposed single-view reconstruction (SVR) method is based on vanishing points and single-view metrology. More detailed models can also be generated according to semantic analysis of the façade images. Experimental results presented in this paper demonstrate that both TLS and SVR approaches can successfully produce accurate and detailed 3D building models from LiDAR point clouds or different types of single-view perspective images.

scholarly journals AUTOMATIC 3D BUILDING MODEL GENERATION USING DEEP LEARNING METHODS BASED ON CITYJSON AND 2D FLOOR PLANS

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 49-54
Author(s):  
R. G. Kippers ◽  
M. Koeva ◽  
M. van Keulen ◽  
S. J. Oude Elberink
Keyword(s):  
Deep Learning ◽  
3D Models ◽  
Life Cycles ◽  
Data Sources ◽  
Floor Plan ◽  
Building Model ◽  
Building Models ◽  
3D Data ◽  
Combination Of Methods ◽  
2D Data

Abstract. In the past decade, a lot of effort is put into applying digital innovations to building life cycles. 3D Models have been proven to be efficient for decision making, scenario simulation and 3D data analysis during this life cycle. Creating such digital representation of a building can be a labour-intensive task, depending on the desired scale and level of detail (LOD). This research aims at creating a new automatic deep learning based method for building model reconstruction. It combines exterior and interior data sources: 1) 3D BAG, 2) archived floor plan images. To reconstruct 3D building models from the two data sources, an innovative combination of methods is proposed. In order to obtain the information needed from the floor plan images (walls, openings and labels), deep learning techniques have been used. In addition, post-processing techniques are introduced to transform the data in the required format. In order to fuse the extracted 2D data and the 3D exterior, a data fusion process is introduced. From the literature review, no prior research on automatic integration of CityGML/JSON and floor plan images has been found. Therefore, this method is a first approach to this data integration.

Representing 3D Model of Building From TLS Data Scanning in CityGML

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
R. Akmaliaa ◽  
H. Setan ◽  
Z. Majid ◽  
D. Suwardhi
Keyword(s):  
3D Model ◽  
Semantic Information ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Models ◽  
Data Sources ◽  
Detailed Model ◽  
Building Model ◽  
Building Models ◽  
3D Building Model

Nowadays, 3D city models are used by the increasing number of applications. Most applications require not only geometric information but also semantic information. As a standard and tool for 3D city model, CityGML, provides a method for storing and managing both geometric and semantic information. Moreover, it also provides the multi-scale representation of 3D building model for efficient visualization. In CityGML, building models are represented in five LODs (Level of Detail), start from LOD0, LOD1, LOD2, LOD3, and LOD4. Each level has different accuracy and detail requirement for visualization. Usually, for obtaining multi-LOD of 3D building model, several data sources are integrated. For example, LiDAR data is used for generating LOD0, LOD1, and LOD2 as close-range photogrammetry data is used for generating more detailed model in LOD3 and LOD4. However, using additional data sources is increasing cost and time consuming. Since the development of TLS (Terrestrial Laser Scanner), data collection for detailed model can be conducted in a relative short time compared to photogrammetry. Point cloud generated from TLS can be used for generating the multi-LOD of building model. This paper gives an overview about the representation of 3D building model in CityGML and also method for generating multi-LOD of building from TLS data. An experiment was conducted using TLS. Following the standard in CityGML, point clouds from TLS were processed resulting 3D model of building in different level of details. Afterward, models in different LOD were converted into XML schema to be used in CityGML. From the experiment, final result shows that TLS can be used for generating 3D models of building in LOD1, LOD2, and LOD3.

scholarly journals Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

2021 ◽  
Vol 14 (1) ◽  
pp. 50
Author(s):  
Haiqing He ◽  
Jing Yu ◽  
Penggen Cheng ◽  
Yuqian Wang ◽  
Yufeng Zhu ◽  
...  
Keyword(s):  
Texture Mapping ◽  
3D Models ◽  
Building Model ◽  
Texture Information ◽  
Building Models ◽  
Photogrammetric Method ◽  
Data Source ◽  
Single Data Source ◽  
Single Data ◽  
Point Line

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.

scholarly journals FOOTPRINT MAP PARTITIONING USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol III-3 ◽  
pp. 241-247
Author(s):  
B. Xiong ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Decision Making ◽  
Laser Scanning ◽  
State Of The Art ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
The Other ◽  
Advantages And Disadvantages ◽  
Airborne Laser ◽  
Building Models ◽  
Height Data

Nowadays many cities and countries are creating their 3D building models for a better daily management and smarter decision making. The newly created 3D models are required to be consistent with existing 2D footprint maps. Thereby the 2D maps are usually combined with height data for the task of 3D reconstruction. Many buildings are often composed by parts that are discontinuous over height. Building parts can be reconstructed independently and combined into a complete building. Therefore, most of the state-of-the-art work on 3D building reconstruction first decomposes a footprint map into parts. However, those works usually change the footprint maps for easier partitioning and cannot detect building parts that are fully inside the footprint polygon. In order to solve those problems, we introduce two methodologies, one more dependent on height data, and the other one more dependent on footprints. We also experimentally evaluate the two methodologies and compare their advantages and disadvantages. The experiments use Airborne Laser Scanning (ALS) data and two vector maps, one with 1:10,000 scale and another one with 1:500 scale.

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